Method and system for customizing allocation statistics

ABSTRACT

A system and method are provided to customize allocation statistics. In one embodiment, a tag is associated to one or more threads to receive allocation statistic information relating to the one or more tagged threads, the one or more threads are tagged prior to performing an allocation statistic session. The allocation statistic session is performed via a plurality of threads at a first virtual machine at a server. A plurality of threads performing the allocation statistic session includes the one or more tagged threads is determined. The allocation statistic information relating to the one or more tagged threads is extracted, if the plurality of threads includes the one or more tagged threads.

FIELD

Embodiments of the invention relate generally to the field of data processing systems. More particularly, the embodiments of the invention relate to customizing allocation statistics.

BACKGROUND

A memory on any computing system is a limited resource. No matter how fast computing systems become, they always depend upon a finite amount of memory in which to run their software applications. As a result, software developers should consider this resource when writing and developing software applications.

The Java programming language differs from many traditional programming languages (e.g., C, C++) by the way in which memory is allocated and deallocated. In languages like C and C++ memory is explicitly allocated and deallocated by the application programmer/developer. This can greatly increase the time spent by programmers in tracking down coding defects in regards to deallocating memory. The Java programming language presents several features that appeal to developers of large-scale distributed systems, such as “write once, run anywhere” portability, portable support for multithreaded programming, support for distributed programming, including remote method invocation, garbage collection, and an appealing object model have encouraged Java use for systems with a size and complexity far beyond small applets. However, the developers of these applications often encounter problems, such as memory leaks, performance and scalability problems, synchronization problems, and programming errors.

Java runtime environments (e.g., Java virtual machine) provide a built-in mechanism for allocating and deallocating memory. In Java, memory is allocated to objects. The Java virtual machine (“VM” or “JVM”) automatically handles the amount and allocation of memory upon an object's creation. The Java runtime environment employs a “garbage collector” (GC) to reclaim the memory allocated to an object that is no longer needed. Once the GC determines that the object is no longer accessible (e.g., when there is no longer any references to it stored in any variables, the fields of objects, or the elements of any arrays, etc.), it reclaims the allocated memory. When objects in a Java application are no longer referenced, the heap space the object occupied is to be recycled so that the space becomes available for subsequently-created objects.

Although having garbage collection improves productivity, it is not entirely immune from a class of bugs, called “memory leaks.” A memory leak can occur when a program (or in the case of Java, the VM) allocates memory to an object but never (or only partially) deallocates the memory when the object is no longer needed. As a result, a continually increasing block of memory may be allocated to the object, eventually resulting in an “Out Of Memory Error” (OOME). In other words, a memory leak occurs when memory is allocated, but it is never (or only partially) reclaimed. Memory leaks can also occur when a data structure (e.g., hashtable) is used to associated one object with another and even when neither object is required any longer, the association with the data structure remains, preventing the objects from being reclaims until the data structure is reclaimed. Stated differently, when a lifetime of the data structure is longer than that of the objects associated with it, memory leaks are caused.

Memory leaks are of particular concern on Java-based systems (e.g., Java 2 Platform Enterprise Edition (J2EE) platforms) which are to run twenty-four hours a day, seven days a week. In this case, memory leaks, even seemingly insignificant ones, can become a major problem. Even the smallest memory leak in code that runs 24/7 may eventually cause an OOME, which can bring down the VM and its applications or even all VMs running on a particular application server instance. This can cause critical performance problems.

It is generally preferred to profile memory use and debug memory leaks in an application code in the early stages of development to provide an early detection of memory problems long before the production stage. Although garbage collection makes code much safer, because having the developer to explicitly delete objects from memory is prone to human error, garbage collection is not a panacea. For example, if the developer does not manage the references to the Java objects carefully, it can result in a memory leak problem, such as a reference to an object is stored within an instance or class field, this reference may exist throughout the life of the application and, unless desired, is regarded a memory leak.

Within a distributed application server environment having thousand of concurrent users, performance and scalability problems are typical. The causes of problems are various, such as synchronization problems, extensive access to shared resources (e.g., database systems), bad configuration settings, etc. To provide consistency within such a system, locks with various validity scopes (e.g., VM-local, application-server-wide, and system-wide) are used; however, deadlock situations and synchronization problems exist.

Several performance monitoring, profiling, and debugging tools are used to examine software applications to determine resource consumption within the Java runtime environment (JRE). For example, a profiling tool may identify the most frequently executed methods and objects created in an application. A type of software performance and debugging tool is a “tracer.” However, such tools are very limited in detecting and exposing system inefficiencies and problems (e.g., memory leaks), while consuming great amounts of system resources by requiring overhead tasks, such as starting and restarting of VMs in special modes. Further, such tools are also limited in providing necessary information about system problems and the limited information that these tools may provide is not useful for applications comprising several thousand objects. This leaves developers with often insurmountable amounts of code to manually evaluate to track down the problem objects/variables, such as the specific class, method calls, etc. For example, conventional profiling tools, like Optimizelt and JProbe, when used, require restarting of VMs and servers, which results in loss of production and system resources, particularly when restarting a productive system. Moreover, the starting of a server and its VMs further adds to the system overhead by increasing memory consumption, which also harms the normal work of the server and server software. The restarting of the server adds overhead in regards to the Central Processing Unit (CPU), as the server would have to start up from scratch.

Conventional profilers consume a great amount of memory and cannot be limited to a certain code section. Furthermore, conventional profilers do not distinguish between free objects and those objects that are still living in the underlying VM. Also, these profilers are to be started in a special way at VM startup that is not compatible with productive environments due to high memory consumption and lack of performance.

FIG. 1 illustrates a conventional profiling tool. Client 102 is in communication with server 108. Client 102 includes a VM 102. Server 108 includes a VM 112, which includes Java Virtual Machine Profiling Interface (JVMPI)-based interface 116 and implementation 114. Server 108 further includes a native/default profiling agent (having an agent library) 110 which is plugged into the VM 112 at start-up. Since JVMPI is a native/default-interface, the agent 110 is also written in native code. An agent 110 refers to a software entity, which is used to gather profiling information native VM interfaces (e.g., JVMPI). JVMPI-based implementation 114 suffers from high memory footprints and, like conventional tools JProbe and Wily Introscope, requires a VM restart. However, conventional profiling tools (e.g., also those using Java Virtual Machine Tool Interface (JVMTI)) cannot be used in productive systems without disturbing user sessions. Further, they cannot be used in large application server environments as they cause high memory consumption. Referring back to FIG. 1, for example, to start profiling traces, the VM 112 is to be restarted in special way, such as by having the agent 110 loaded at VM-startup, which can cause negative impact on performance and memory consumption. There are merely some of the limitations of conventional profiling solutions. Similarly, conventional monitoring tools and debugging tools (e.g., using Java Virtual Machine Debugging Interface (JVMDI)) also suffer from these and additional limitations.

SUMMARY

A system and method are provided to customize allocation statistics. In one embodiment, a tag is associated to one or more threads to receive allocation statistic information relating to the one or more tagged threads, the one or more threads are tagged prior to performing an allocation statistic session. The allocation statistic session is performed via a plurality of threads at a first virtual machine at a server. A plurality of threads performing the allocation statistic session includes the one or more tagged threads is determined. The allocation statistic information relating to the one or more tagged threads is extracted, if the plurality of threads includes the one or more tagged threads.

The above attributes may be implemented using a computer program, a method, a system or apparatus, or any combination of computer programs, methods, or systems. These and other details of one or more embodiments of the invention are set forth in the accompanying drawings and in the description below.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is illustrated by way of example and not by way of limitation in the figures of the accompanying drawings in which like references indicate similar elements. It should be noted that references to “an” or “one” embodiment in this disclosure are not necessarily to the same embodiment, and such references mean at least one.

FIG. 1 illustrates a conventional profiling tool.

FIG. 2 illustrates an embodiment of a server having an embodiment of an on-demand profiling infrastructure.

FIG. 3 illustrates an embodiment of a backend VM having an embodiment of an on-demand profiling infrastructure.

FIG. 4 illustrates an embodiment of a process for profiling using an embodiment of an on-demand profiling infrastructure.

FIG. 5A illustrates an embodiment of a mechanism for performing and customizing an allocation statistic trace using a tagging mechanism.

FIG. 5B illustrates an embodiment of a hash map of heap addresses.

FIG. 6 illustrates an embodiment of a transaction sequence for performing and customizing an allocation statistic trace using a tagging mechanism.

FIG. 7 illustrates an embodiment of a process for performing and customizing an allocation statistic trace using a tagging mechanism.

FIG. 8 illustrates an embodiment of a monitoring tool.

FIG. 9 illustrates an embodiment of a computing system.

FIG. 10 illustrates an embodiment of a client/server network system employing a message enhancement mechanism.

DETAILED DESCRIPTION

As used herein, references to one or more “embodiments” are understood as describing a particular feature, structure, or characteristic included in at least one implementation of the invention. Thus, phrases such as “in one embodiment” or “in an alternate embodiment” appearing herein describe various embodiments and implementations of the invention, and do not necessarily all refer to the same embodiment. However, they are also not necessarily mutually exclusive. Descriptions of certain details and implementations follow, including a description of the figures, which may depict some or all of the embodiments described below, as well as discussing other potential embodiments or implementations of the inventive concepts presented herein.

Java applications can vary in both size and complexity. In addition, certain large Java application (e.g., ˜10,000 classes and ˜1,000,000 methods with ˜100,000,000 method calls) may run 24/7 (“long living” applications). Within a long living application, major problems (e.g., memory leaks) are expected to occur in terms of both stability and performance. For example, a single long living object that increases in size by 1 byte between each GC cycle will eventually cause the application and VM to crash due to an OOME. Although such a crash may take a long time (e.g., 1 bytes per GC cycle*millions of free bytes of memory), it will inevitably occur. Furthermore, when dealing with such long applications and productive systems, mere use of commercial and non-commercial conventional profiling tools and debugging tools having JVMPI and JVMTI profiling interfaces and JVMDI debugging interface, respectively, are not suitable and cannot provide the necessary profiling, debugging, and monitoring information. Even when dealing with suitable systems, such conventional tools cause high memory footprints and are not effective without having to restart the VM and are known to disturb user sessions inherent to the VM.

In one embodiment, a vendor-specific proprietary interface and implementation are provided, as described throughout this document (e.g., see FIG. 1). This implementation can be made an integral part of a VM (e.g., JVM, SAP JVM) or any program understanding the protocol and allow for on-demand examining of system problems, including in productive systems, without restarting the underlying VM. These system problems can range anywhere from memory leaks to performance, scalability and synchronization problems. In one embodiment, “on-demand” refers to examining (e.g., profiling, tracing, debugging, and/or monitoring) system problems in runtime, such as without the need for restarting the underlying VM.

FIG. 2 illustrates an embodiment of a server 202 having an embodiment of an on-demand profiling infrastructure 208. Sever 202 comprises a backend VM 206 (e.g., JVM, SAP JVM) having an embodiment of an on-demand profiling framework or infrastructure (profiling infrastructure) 208. Profiling infrastructure 208 is shown in communication with a server Java application programming interface (API) 204. In one embodiment, profiling infrastructure 208 is implemented as an intrinsic and direct part of the underlying VM 206 and is embedded within the backend VM 206, rather than relying on native profiling interfaces, such as JVMTI and JVMPI, and agent, for implementation. Since the profiling infrastructure 208 is an intrinsic part of the backend VM 206, no additional memory overhead is needed. Java API 204 allows for starting and stopping of the profiling backend VM 206. In one embodiment, backend VM 206 (e.g., Java VM) resides at server 202 (e.g., J2EE server).

In one embodiment, profiling is performed using profiling infrastructure 208 that resides at backend VM 206 that is being profiled. Profiling infrastructure 208 includes a number of components (as described in FIG. 3) to perform trace profiling. In one embodiment, using profiling infrastructure 208, no default profiling agent or default implementations and instances (e.g., JVMPI, JVMTI) are needed or employed. Without having the default agent employed, a direct communication is established between backend VM 206 and frontend VM 214 via server Java API 204 and client Java API 212 and profiling protocol 210. Any number of VMs may be used as backend or frontend VMs. Furthermore, when performing profiling trace in an offline profiling mode, an external profiling file 218 is used to store profiling trace data. Starting and stopping of profiling trace may be performed in a number of ways, such as using a Graphical User Interface (GUI)-based monitoring tool 220. The profiling data is written using various components of profiling infrastructure 208 and displayed to the user using any number of display devices. These display devices may include GUI-based display devices. In one embodiment, using profiling infrastructure 208, on-demand profiling is performed which refers to performing the profiling without restarting the underlying VM 206. Stated differently, the profiling is performed in runtime without any interruptions or restarting of the underlying VM 206.

Profiling infrastructure 208 can be used for starting profiling traces for certain users or applications, such as using profiling annotations. Profiling annotations refer to a concept of tagging threads with certain semantic information from an application server environment. Here, Java API 204 is provided which allows for annotating a Java thread with one or more of the following information: user name, application name, request identifier, and session identifier. If profiling traces are started, a thread filter for such information is provided and thus, a profiling trace can be started only a certain user or application. A Java API is also provided on the client-side, such as client Java API 212, that communication with server Java API 204 via a profiling protocol 210. Client 216 includes frontend VM 214, which includes any arbitrary VM that represents a native application that speaks (e.g., in case of online profiling) the profiling protocol 210 and/or knows (e.g., in case of offline profiling) the profiling file format of profiling file 218. Backend VM 206 is the one that is being profiled.

It is to be noted that the VMs 206, 214 may not be VMs and instead be any program or application (e.g., a native application or program) that is compatible with the components of and related to the profiling infrastructure 208. For example, the frontend VM 214 is illustrated here merely as an example for brevity and clarity. It is, however, contemplated that a frontend VM 214 or any VM for that matter is not necessary for embodiments of the present invention. For example, in one embodiment, instead of employing a VM 214, any program or application that is compatible with the mechanisms and components described herein is acceptable and functional and can be employed and implemented. Stated differently, for example, any program that can read and speak the described components (e.g., components of profiling infrastructure 208), protocols (e.g., socket communication protocol), APIs (e.g., server- and client-side APIs 204, 212), parameters, profiling files 218, etc., is compatible and can be used instead of a VM, such as the frontend VM 214. This is applicable throughout this document wherever there is mention of a VM 206, 214.

The illustrated mechanism 200 provides both an online mechanism for (interactive) profiling and an offline mechanism for (non-interactive) profiling. When starting profiling the backend VM 206, any profiling parameters including the desired mode, e.g., an online or offline mode, are specified. If started in the online mode, the profiling backend VM 206 opens a port and waits for a connection. The profiling frontend VM 214 attach to this connection via the profiling protocol 210 and Java APIs 204, 212. The starting, running, and stopping of profiling and tracing is then performed. In one embodiment, online profiling is performed via internal components, such as Java APIs 204, 212, or external components, such as a monitoring tool (e.g., Java VM monitor) 220. Online profiling may also be performed using a command line, such as java -agentlibjdwp transport=dt_socket,address=8000,suspend=n or bin\java-monjdwp:transport=dt_socket,address=8000,server=y. For the offline mode, profiling files 218 are used to store profiling data and a special interface is provided to couple the backend VM 206 with the frontend VM 214 via client Java API 212 to allow for starting and stopping of traces. In some cases, server Java API 204 can also be used to perform offline profiling. Offline profiling may also be performed using monitoring tool 220 and/or using a command line, such as java -XX:+Profiling-XX:+ProfilingAlloationTrace.

When the profiling mechanism 200 is started in the offline or non-interactive mode, the profiling information is stored in an external medium 218 (e.g., file system) and can be analyzed after the profiling run. This way, the profiling information may then be used for port-mortem analysis; however, traces can still be started and stopped in an interactive manner. In contrast, the online or interactive mode allows for analyzing the profiling information online. For example, if a class statistic trace has been enabled and a garbage collection happens, the profiling information can be directly accessible through a stream-based interface.

Furthermore, to have no performance degradation in case of running in a non-profiling mode (e.g., when no profiling is being performed), VM 206 may maintain a global flag indicating whether profiling is enabled or not. The flag may be requested each time any profiling data is written. For example, a profiling trace for garbage collection events may be implemented in the following way: when a garbage collection is performed, the global profiling flag is checked. If profiling is enabled, the flag is checked to indicate whether garbage collection events are to be profiled. This can also be done via some VM global flags. If the garbage collection trace is enabled, the backend VM 206 may be called to collect the desired data.

FIG. 3 illustrates an embodiment of a backend VM 206 having an embodiment of an on-demand profiling infrastructure 208. In one embodiment, profiling infrastructure 208 contains controller framework 302, thread filter 304, buffer framework 306, class filter 308, identification service 310, communication framework 312, object identification service 314, allocation trace module 316, loitering trace module 318, garbage collection trace module 320, and other trace modules 322 to perform other traces.

In one embodiment, profiling controller framework 302 is used for starting and stopping profiling runs and traces. Controller framework 302 allows the user to specify profiling options or settings that the user would want to enable. These profiling settings to be applied are divided into distinct areas, such as functional profiling settings and filter settings. The functional profiling settings determine the area to be profiled (e.g., allocation trace, reference trace, etc.), while the filter settings define the validity scope (e.g., user, session, thread, VM, etc.) of the functional profiling settings. For example, an allocation trace can be started for a specified user. Java API and graphical user interface (GUI) are provided in communication with profiling controller framework 302. GUI is used to enable the user to directly specify the desired profiling settings without any system-guidance. Additionally, a wizard-similar interface is provided. GUI also allows for an expert mode and for a wizard-guided mode. Controller framework 302 may include a profiling evaluation module for analyzing a performed profiling run. For example, the Java API can be used for getting the complete low-level profiling information gathered within a corresponding profiling run as well as for getting condensed, problem-oriented profiling information. The condensed profiling information may be used to directly pinpoint various problematic areas. For example, if the user has performed performance analysis using a time-based sampling approach, the Java API may enable a client to directly receive information about the time-consuming methods. The user may view this information via GUI at a display device at the client.

Controller framework 302 is used for starting and stopping profiling runs and traces, which includes starting and stopping various profiling options (further described later). For each profiling run the user is free to determine the set of traces to be started. For example, the user may start an allocation trace using the allocation trace module 316 together with a class statistic trace. A user-defined name may be assigned to each non-interactive profiling run and used later on to evaluate the gathered profiling information. Considering interactive profiling runs, the user is able to evaluate the profiling information online and therefore, the profiling information may be available through a stream-based interface.

Furthermore, controller framework 302 may be independent of the surrounding application server environment. Stated differently, controller framework 302 refers to the underlying VM 206 currently executing a profiling request (e.g., starting an allocation trace). The corresponding application server infrastructure may be responsible for starting and stopping the desired trace on other VMs. For example, if an allocation trace is started for a certain user session at VM 208, the application server infrastructure accounts for starting the allocation trace in the VMs executing requests for the user session. Controller framework 302 enables the application server infrastructure to specify thread filters 304. A thread filter 304 may contain the following information: client, user, session identifier, request identifier, application name, and component name. On the one hand, controller framework 302 may provide a facility to tag these pieces of information to a thread. On the other hand, if a certain profiling run is to be started, a thread filter 304 is provided. Hence, for example, a trace may be stared only for a certain user. Accordingly, the application server is responsible for setting the current thread state (e.g., client, user, session identifier, etc.). In one embodiment, an application server includes a J2EE server.

In one embodiment, the profiling options include functions/cases, such as memory debugging (e.g., memory leak detection), performance analysis, synchronization monitoring, and application debugging (e.g., detecting called methods). These profiling functions further include a number of sub-functions, such as heap dump, coupling of debugging and profiling infrastructure, time-based sampling, memory-based sampling, method statistic, allocation trace, silent allocation trace, allocation statistic trace, loitering trace, garbage collection trace, garbage collection statistic, class statistic trace, permanent generation statistic trace, local garbage collection trace, shared garbage collection statistic, other traces, such as reference trace, object death trace, object movement trace, shared closure trace, global reference trace, method trace, time method trace, input/output (I/O) trace, monitor trace, shared lock trace, method count trace, execution line trace, scheduler trace, and exception trace.

Solving a memory leak problem may include a couple of processes, such as identifying the Java classes or objects caused the memory leak, and determining where in the infrastructure or application code the leak occurred. Many of the sub functions can be used to solve memory leak problems. Class statistic trace functionality is provided to help identify the Java classes that cause memory leaks. Class statistic trace includes getting an overview of all living classes within particular VM, including class name, class loader description, the number of object instances, and the accumulated net and gross size of all object instances. The information may be traced after each full local garbage collection. Reference trace includes detecting the objects holding references to leaking objects. It also provides the complete reference chain to a specific object instance. This information may also be available after one full local garbage collection.

If the class statistic trace reveals that specific objects are created over and over again, using the allocation trace module 316, the allocation trace may be enabled to check for the exact allocation place. Using the allocation trace module 316, the allocation trace enables the user to specify a class filter 308. Silent allocation trace is a derivate of allocation trace. When an allocation trace is started, each object, which is allocated and adheres to a user-defined class filter 308, is assigned to an object identifier. Although the allocation trace enables the user to get informed about object allocations, the user may not get the information when the corresponding object dies. In that case, object death trace allows the user to check for those objects are garbage collected and no longer alive. Object movement trace makes allows the checking of why certain objects are kept alive, while the allocation trace allows for getting information when certain objects are created.

Shared closure trace provides for getting object information each time shared closures are created, deleted, copied or mapped. Global references may be used across multiple invocations of a Java Native Interface (JNI) method and also across multiple threads. A global reference remains valid until it is freed by the programmer and ensures that the referenced object is not garbage collected. For relatively complex scenarios, a dump of the current Java heap is performed. The heap dump function allows for getting a dump of the current, overall object state.

In some cases, memory leaks occur due to the fact that a failed clean-up operation. For example, considering a cache based on shared closures, at regular intervals, the cache might be cleared. If the clean-up operation were interrupted at the end of the operation (e.g., due to a VM abort exception), most cache entries would probably be deleted; however, some entries might still exist. Thus, a memory leak may be resulted if the cache were not able to remove any of the existing entries. The detection of this kind of memory leak could be difficult, since most object instances of the corresponding class are removed and merely a few exist. Thus, class statistic trace may not be the right choice to detect such a memory leak. One characteristic of this problem is that the memory leak is caused by objects which may not be used any longer. The loitering trace performed via loitering trace module 318 facilitates the detection of objects which are not used for a long time.

Various performance problems may be caused by any number of reasons, such as choosing the wrong algorithm for a problem, repeatedly recalculating the same result, excessive allocating of temporary objects, too many I/O operations or transferring too much memory, etc. Profiling helps improving the performance by determining what is it that is to be optimized. Profiling identifies parts of the overall system for which optimization can have an impact on the overall performance. Optimizing a function which only amounts to a miniscule fraction of the overall runtime may not have noticeable benefits. Profiling also determines how the optimization is to be done. Checking for optimization options of those parts that are identified during the first process. Time-based sampling is used to get an overview of methods, which consume the most CPU resources of the application. Time-based sampling works by dumping a stack trace of the currently active thread at regular intervals. Memory-based sampling works analogously to the time-base sampling; however instead of dumping a stack trace in time intervals (Δt), stack trace is sampled after an amount of memory (ΔM) is allocated on the Java heap. This way, those methods that allocate the largest number of bytes on the Java heap are identified.

When time-based sampling shows that a method uses a large amount of time, the reason for this resource consumption might be that a call of the method is expensive or the method is called very often. To find out how many times a particular method was called, method statistic trace may be used. Together with time-based sampling, method statistic trace may also allow for calculating the average runtime of a specific method (e.g., the “cumulative time” divided by the method count). Method trace is used to get more detailed information than method statistic. Time method trace can be used to provide very detailed trace information. Time method trace provides for detecting method calls that (for any number of reasons) take a particularly long time. To see, if garbage collection is properly configured or if a particular problem related to garbage collection exists, local GC statistic is used, which includes dumping a statistical entry for each local garbage collection (partial and full) for each garbage collection run. Shared GC statistic is emitted when a local GC detects that a shared GC has happened and has not been dumped yet. The shared GC statistic contains the number and size of the collected shared classes, shared interned strings, and shared classes.

Another source of performance problems is related to I/O. These I/O-related problems include a network connection being operated at its bandwidth maximum, the latency being too high, an external system being overloaded, etc. To check for an I/O problem, I/O trace allows for tracing the timing of each I/O operation. I/O trace can be used in analysis to check for operations, where huge amounts of data were transmitted, the I/O operation took an extraordinary amount of time, or a huge amount of small I/O operations was performed.

Java has an explicit support for multithreading and concurrency at the language level. Although these welcome features, the typical problems with multithreading and concurrency are deadlocks, race conditions, thread starvation, and scalability problems. Synchronization monitoring is provided to detect such problems. For example, synchronization monitoring includes monitor trace that identifies deadlock or scalability problems and gathers information about locks used inside a VM. To find synchronization problems, a thread trying to acquire a lock is identified and once it is identified, the lock is freed by the thread. Shared lock trace is used to identify deadlocks between VMs and scalability problems of a server instance. Shared lock trace provides information about different kinds of shared lock activities, like entering and leaving. Further, for such problems above, scheduler trace is used to know why a thread was scheduled and why it gave up control of the CPU, and for how long the entire VM was waiting on external I/O or just sleeping.

In one embodiment, application debugging is used to provide those the debugging functionalities that are not supported by conventional debugging instances and protocols, such as JVMDI, Java Debug Wire Protocol (JDWP), etc. For example, application debugging covers functionalities, such as call coverage and line coverage. Regarding call coverage, method count trace may deliver a number of calls to a method. Regarding line coverage, execution line trace may deliver information about code lines that were executed. Method call trace is used to find all methods that are called. When the method call trace is enabled, the VM 206 counts method calls and when the method call trace is disabled, the VM 206 dumps the collected information, such as name and signature of a method and the number of times it was called. Execution line trace may be used to find out the lines of code that are not executed. When the execution line trace is triggered, it enables the VM to write out information about the method and code line each time a byte code is interpreted and/or the line number changes. Such information can help the developer find out the lines of code that are not covered particular test cases.

Method trace may be employed to trace or profile the debugging process of an application. For example, the method trace is used to find out what has happened before the program reaches a certain point. Such information may be used to trace back the program flow and find out in which way the program reached that point of code. Exception trace is another functionality that may be employed to trace or profile the debugging process of an application. This information can be used to trace back the reasons for exceptions that followed up and for different execution branches.

In one embodiment, a dedicated Java API and a GUI is provided to allow for starting and stopping of various functionalities and uses (e.g., allocation trace, loitering trace, GC trace, and other traces) and for getting the corresponding profiling and tracing results. To determine and analyze the profiling and tracing results, an expert mode and/or a guided mode are provided. For example, a guided mode may directly pinpoint any problem areas.

Profiling infrastructure 208 is compatible with multiple clients. For example, depending on the surrounding application server infrastructure and whether any clients are handled in a special way, the profiling infrastructure 208 may perform in compliance with several clients, simultaneously, and remain multiple client-compliant. Profiling infrastructure 208 also allows for restricting profiling runs to certain clients, while the surrounding application server environment may assure that the current client information is assigned to the respective thread. Furthermore, profiling infrastructure 208 may be started on-demand, which includes performing profiling infrastructure functionalities (e.g., profiling, tracing, etc.) without restarting the entire application server or even the underlying VM 206. If no profiling option is enabled by a certain user, there is no impact on the response time caused by the profiling infrastructure 208. However, if profiling is enabled, it may depend on the started profiling options and filter settings about how the overall system performance is influenced. For example, if a method trace is started on an application server without any filter settings (e.g., user, classes, etc.), the performance may decrease to an extent. Therefore, the profiling infrastructure 208 as well as the application server infrastructure must provide options to restrict profiling runs. This way, profiling may be enabled for a particular user or session, while users and sessions remain unaffected. In addition, profiling infrastructure 208 provides reasonable and necessary filter settings for various profiling traces.

Class filters 308 are implemented to allow for limiting profiling trace outputs by limiting the process of profiling to, for example, specific traces. For example, if a developer seeks to profile only Java object allocations which refer to java.lang.HashMap instances, then, using class filters 308, a profiling allocation trace with a class filter applying exclusively to java.lang.HashMap instances is started. Thread filters 304 relate to profiling annotations (e.g., specifying annotations), such as when an allocation trace exists. Thread filters 304 may also be used by the user to specify when and/or where a trace is to be triggered and/or used. Buffer framework 306 is used to compress and decompress any type of data or information that is being communicated, stored, etc. Communication framework 312 is used to facilitate communication of any data or information between and within various components, elements, modules, systems, servers, VM, etc. Communication framework 312 is also used to determine and facilitate the storing of data or information, such as storing the data using files or socket connections.

ID service 310 is employed to specify variables, such a class, a name of the class, etc. to assign identification to them. Once class, class names, etc. are assigned an ID (e.g., a number), they are then mapped with each other and with various components and variables via a mapping packet, instead of mapping by names. Using ID service 310, the same can be done with threads and methods. For example, by assigning IDs (instead of names) to threads and methods, when dumping is performed, the IDs of threads and methods are dumped rather than their names. This technique of using IDs (e.g., numbers) instead of using the names is efficient, fast, and saves memory.

For example, an allocation event is considered. ID numbers are mapped to various packet names, such as java.Hashtable is mapped to “2000”, the thread (named, “main”) is assigned “3”, and the user (named, “Hansi”) is assigned “7”. Stack trace is then commenced using command lines, such as com.sap.test (line 30), com.sap.submethod (line 2003), etc. The even information may then be provided as 2000, 3, etc. It is known that ID number 2000 was mapped to the underlying hashtable, while ID number 3 was mapped to the thread. Using these ID's, names (e.g., main, Hansi, etc.) are not needed and instead, IDs are used, which provides an easier technique for packet name mapping. Similarly, object ID service 314 is used to assign IDs (e.g., numbers) to objects so the IDs can be used to, for example, identify and compare the objects, instead of using object names.

In one embodiment, profiling information and any other relevant data is displayed at a display device via GUI at a client so that a user can access and evaluate the displayed information. The information may also be stored at a database and/or file system for subsequent retrieval and analysis. Although Java components, such as J2EE server, Java VM, Java heap, and Java memory errors, etc., are discussed here for simplicity and brevity, it should be noted, however, that the underlying principles and embodiments of the present invention may be implemented within any type of object-oriented and runtime environments. Moreover, it should be noted that requirements and examples used in this document do not necessarily reflect the real values that a system or program would actually produce. For example, garbage collection may be invoked multiple times while checking the VM heap memory size, so that there are different VM implementations and, according to a relevant VM specification, a given VM implementation might not clean up the memory immediately after it has been requested to do so. Thus, to be sure that a memory cleanup is provoked, the memory size may be checked and the garbage collection may be invoked again, as necessary.

Garbage collection as described here includes a process designed to identify and reclaim blocks of memory that are dispensed by a memory allocator but are no longer “alive” or “live” (e.g., no longer being used, as determined, for example, by not being reachable from any currently referenced objects or entities). Garbage collection can sometimes be handled as a background task by runtime systems rather than as an explicit task by user programs. Garbage collection can also be handled as an inlined task. Garbage collection can be used to reclaim memory in runtime systems, and there are some well-known garbage collection algorithms (e.g., reference counting, mark-sweep, mark-compact, and copying algorithms).

A VM (e.g., VM 206) is an example of a runtime system. A VM refers to an abstract machine that includes an instruction set, a set of registers, a stack, a heap, and a method area, such as a machine or processor. A VM essentially acts as an interface between program code and the actual processor or hardware platform on which the program code is to be executed. The program code includes instructions from the VM instruction set that manipulates the resources of the VM. The VM executes instructions on the processor or hardware platform on which the VM is running, and manipulates the resources of that processor or hardware platform, so as to effect the instructions of the program code. For example, a Java source program can be compiled into program code, such as bytecode. Bytecode can be executed on a VM, such as JVM, running on any processor or platform. The JVM can either interpret the bytecode one instruction at a time, or the bytecode can be further compiled for the real processor or platform using a just-in-time (JIT) compiler.

The illustrated VM 206, 214 includes a JVM (e.g., SAP JVM), which is used as an example; however, other examples of VMs, which can be used in various embodiments, include Advanced Business Application Programming (ABAP) language VMs, Common Language Runtime (CLR) VMs, and the like. ABAP is a programming language for developing applications for SAP systems, such as SAP R/3 system, which is a widely installed business application system developed by SAP AG of Walldorf, Germany. The CLR is a managed code execution environment developed by Microsoft Corp. of Redmond, Wash. For simplicity and brevity, the discussion in this document focuses on virtual machines, and in particular Java virtual machine 104, but it is to be understood that the techniques described herein can also be used with other types of runtime systems. Furthermore, a VM 206, 214 includes any number of application programs compatible with the embodiments of the present invention and understanding of the protocols.

A runtime system includes a code execution environment that executes instructions or code in user requests and that provides runtime services for that code. Code runtime services may include functionality, such as process, thread, and memory management (e.g., laying out objects in the server memory, sharing objects, managing references to objects, and garbage collecting objects). Enhanced runtime services may include functionality, such as error handling and establishing security and connectivity.

The illustrated server 202 includes a J2EE server/engine/node, which supports Enterprise Java Bean (“EJB”) components and EJB containers (at the business layer) and Servlets and Java Server Pages (“JSP”) (at the presentation layer). It is understood that processes taught by the discussion above can be practiced within various software environments such as, for example, object-oriented and non-object-oriented programming environments, Java based environments (such as a J2EE environment or environments defined by other releases of the Java standard), other environments (e.g., a .NET environment, a Windows/NT environment each provided by Microsoft Corporation), and the like.

FIG. 4 illustrates an embodiment of a process for profiling using an embodiment of an on-demand profiling infrastructure. At decision block 402, whether profiling need be started is determined. If not, the non-profiling mode is maintained, which saves system resources. If the profiling is to be started, the profiling mechanism is switched to the profiling mode at processing block 408. At decision block 410, a determination is made as to whether online or offline profiling is to be performed. If the profiling is to be performed online, the online profiling mode is triggered for, for example, interactive profiling at processing block 412.

Online profiling is started via Java APIs (e.g., server and client Java APIs), using monitoring tools (e.g., Java VM monitor), and/or using command lines. If the profiling is to be performed offline, the offline profiling mode is triggered for, for example, non-interactive profiling. Offline profiling can also be started using monitoring tools, using command lines, and/or via Java APIs as described above. At processing block 420, any profiling data obtained from offline profiling is stored at an external source, such as a profiling file. The data at the profiling file may be stored in zipped format. At decision block 414, whether the process of profiling be continued is determined. If yes, the profiling continues with decision block 410. If not, the profiling status is switched to the non-profiling mode at processing block 416. This non-profiling mode is maintained at processing block 406.

FIG. 5A illustrates an embodiment of a mechanism 500 for performing and customizing an allocation statistic trace using a tagging mechanism. The illustrated embodiment includes a Java application server environment having an application server 502 (server) that may include a J2EE engine. The server 502 having the J2EE engine further includes a backend VM 206 that is the underlying VM wherein profiling sessions are performed. The backend VM 206 includes profiling infrastructure 208 in communication with a frontend VM 214 at client 506 via server and client Java APIs 204, 212. In one embodiment, a mechanism for customizing allocation statistic trace is provided to obtain feedback from the underlying VM 206 about the amount of memory and the number of objects (e.g., “processing space”) that are allocated for a certain processing session for a user (e.g., developer, administrator) to analyze and evaluate. The customizing of allocation statistic trace further includes obtaining the amount of memory and the number of objects are still referenceable (e.g., “hold memory space”) for the user. This information, also referred to as allocation statistic(s) or allocation statistic information, can be used in finding (1) memory leaks if the hold memory space is, contrary to expectations, e.g., greater than 0, and (2) problems with the processing space if too many objects or too much memory are allocated. Since problems can be severe for the system, customizing allocation statistic trace and having the resulting information can be vital for the user and the system. For example, memory leaks could cause the VM to run out of memory and, on the other hand, if the processing space is too large, the garbage collector is triggered frequently. Although these issues can result in a negative impact on response times and overall request throughput, they can be resolved using the allocation statistic trace information as obtained by customizing allocation statistic trace according to one embodiment.

It is desirable to directly obtain a feedback (e.g., allocation statistic) about the memory consumption of an implemented component during development. Customizing allocation statistic trace allows for obtaining desired and customized allocation statistics about the used processing space and hold memory during a user-defined period. The used processing space refers to the number and size of allocated Java objects during a user-defined period, while the hold memory refers to the number and size of Java objects that are allocated during a user-defined period and are still referenced. In one embodiment, a tagging process is provided which includes, to define a period, a user annotating a thread with an allocation statistic identifier (e.g., a number). The tagging information (e.g., the identifier that is associated with the thread) obtained from the tagging process is provided at and extracted from the application server 502 via a tagging information module 504. The tagging information is then communicated to object ID service 314 at the profiling infrastructure 208. That is done so that when an allocation statistic trace is started, each allocation is ascribed to the current thread allocation statistic identifier. If this identifier is set to −1, the allocation is not ascribed to any statistic. When stopped, the allocation trace returns allocation statistics for each assigned allocation statistic identifier. Object ID service 314 is used to identify the tagged identifier to further identify the associated thread, while a trace module (e.g., trace modules 322, allocation trace module 316, etc., of FIG. 3) is used to perform an allocation statistic trace, while controller framework 302 of FIG. 3 is used for performing starting and stopping of and other functions relating to the allocation statistic trace.

Various parameters for customizing and performing the allocation statistic trace may be provided. For example, an allocation statistic identifier assigned to the underlying thread. For each assigned allocation statistic identifier the following values may also be provided: the number of allocated Java instances, the size of allocated Java instances, the number of allocated Java instances that are still referenced, and the size of allocated Java instances that are still referenced.

Given that allocation statistic trace is supported by the on-demand profiling infrastructure 208, the allocation statistic trace and its customization is performed in runtime and without having to restart the underlying VM 206 and thus, not particular startup parameters are to be specified. Along with the controller framework, Java APIs 204, 212 are used to control (e.g., start/stop) the allocation statistic trace. A user may specify a code section for which allocation statistics are to be obtained. The term “allocation statistic” may also refer to a structure containing the following four values: the number of Java object instances allocated during and for the specified code section, the overall size of Java object instances in bytes allocated during and for the specified code section, the freed number of Java object instances allocated during and for the specified code section, and the overall size of freed Java object instances allocated during and for the specified code section.

An allocation statistic may refer to a specific code section that has a start point and an end point, and during this code section, Java object allocations are assigned to this corresponding allocation statistic. If the allocation statistic trace is stopped, a determination is made as to whether the objects assigned to the allocation statistic are still alive. To specify a code section for which a user wants to get an allocation statistic, a tagging mechanism is used to annotate a thread (e.g., Java thread) with a thread tag. The tagging information is provided at the application server 502 using the tagging information module 504, and the tagging information is then extracted from the application server 502 using the tagging information module 504 and communicated to the object ID service 314 via one or more APIs and communication modules. The thread tag refers to a short or small value. For example, a value of “−1” indicates no statistic is desired. If the allocation statistic trace is started in the underlying VM 206, an allocation statistic is provided for each of the specified thread tags. Using this technique, if a Java object gets allocated in a thread that is annotated with a thread tag, the allocation event gets assigned to the allocation statistic of that particular thread tag. A simple pseudo-code example looks as follows:

// annotate the current Java Thread with a thread tag Vm.setTagForCurrentThread((short) 4711); // start the allocation statistic trace AllocationStatisticTrace.start(); ... // the code section to get profiled Vm.setTagForCurrentThread((short) −1); allocationStatistic = AllocationStatisticTrace.stop( );

Using the mechanism 500 for performing and customizing allocation statistic trace, particularly when employing in development and productive environments, memory leaks problems and any processing space problems, if too many objects or too much memory are allocated, can not only be detected, but also using allocation statistics, these problems can be resolved and prevented from occurring in the future. For example, a first hint of problems with the processing space, memory leaks, and/or performance problems is obtained via allocation statistics. Allocation statistic trace are performed at the underlying VM 206 in runtime (e.g., without having to restart the VM 206) and once the first hint of a problem is obtained via the resulting allocation statistics, additional profiling sessions or traces may be performed for detailed analysis of the problem.

In one embodiment, threads (e.g., Java threads) are associated with tags having short identifiers and these tags indicate whether allocations or CPU usage is to be traced for these specific threads. Tags are also used to identify specific code frameworks. Controller framework at the profiling infrastructure 208 may be used to start and stop and perform various allocation statistic traces. The profiling infrastructure may also include a global controller framework associated with the controller framework to help perform various profiling sessions (e.g., allocation statistic trace). In performing the allocation statistic trace, for each thread tag, allocation statistic and time statistic are provided. The allocation statistics include a number of allocated objects (e.g., Java objects) or object instances that are allocated during and to the running of a specified code section, a size of the allocated objects that is provided in number of bytes, a number of freed objects detected during the running of the specified code, and a size of the freed objects in number of bytes. The time statistics include CPU time consumed (e.g., in nanoseconds) during the running the specified code section. These results, having allocation statistics and time statistics, are provided to the frontend VM 214 at the client 506 via the Java APIs 204, 212 to be viewed by the user via a viewer (e.g., a GUI-based viewer) and displayed by a display device at the client 506.

Referring back to thread tagging, for example, a C++ representation of a Java thread may be associated with a thread tag and thus, a VMInfo.setThreadTag( ) calls results in a native call. If a tag is set for an arbitrary thread (e.g., not the current thread), each thread may be synchronized, such as a VM operation is performed. Further, if a tag is requested of an arbitrary thread (e.g., not the current thread), each thread is synchronized, such as a lock over each thread may be acquired. Using the controller framework, if the allocation statistic trace is started, the thread local allocation buffers (“tlabs”) of each thread may be removed and so the objection allocations performed in tagged threads are synchronized, while the non-tagged threads are not affected. If a thread is tagged while the allocation statistic trace is running, the corresponding tlab is removed, accordingly. Furthermore, each time an object (e.g., array, class, instance, etc.) gets allocated, a determination is made as to whether the allocation statistic trace is enabled and a valid tag is associated with the thread. If the trace is enabled and the tag is valid, the profiling infrastructure 208 is notified. Then, the profiling infrastructure 208 stores a mapping between an object (e.g., Java object (“oop”)) and the tag. In one embodiment, the tag is not directly associated with the Java object to keep the Java object from getting bigger, such as by not adding a new filed to the Java object to provide a direct mapping between the Java object and the tag. In another embodiment, a direct mapping between the objects and the corresponding tags is provided.

In one embodiment, once the validity of the thread tag is determined, garbage collection is performed at the backend VM 206 using a garbage collector. During garbage collection, any living objects (e.g., oops) are forwarded to new addresses. The stored mapping between the objects and the tags may then be updated by removing any garbage collected dead objects and applying new addresses to the living objects. Also, for each garbage collection process, a different implementation may be provided. If no direct mapping between the Java objects (e.g., oops) and the tags is provided (as discussed above), a hash map implementation may be provided by storing a corresponding tag for each object.

Referring now to FIG. 5B, it illustrates an embodiment of a hash map 550 of heap addresses 510. As illustrated, for each address area 502-508, a chain of bucket addresses entries is stored. Each bucket entry contains a fixed number of object (e.g., oop) and tag mappings. Any number of such mappings (e.g., eight mappings) may be stored per entry. Also, for example, knowing the start bits of an address 510 for an object address, merely the lower bits of an address may be stored in a bucket entry. Using this mapping technique, lower memory consumption and better cache behavior is achieved. Further, a better performance is achieved since merely the affected objects may be considered, reducing a great deal of system overhead. Also, a full tlab support could be performed; however, a complete garbage collection may not be necessary to compute the living objects and thus, a mark phase may be sufficient and the expensive compact phase may be avoided.

It is contemplated that the illustrated Java-based components, such as the Java application server 502, the Java VMs 206, 214, etc., are provided as examples and that the mechanism 500 and other embodiments of the present invention can also be used with other non-Java-based environments and components. Furthermore, a backend VM 206 may reside at the same J2EE engine as the tagging information module 504 or at another J2EE engine or at another server. Server 502 and client 506 may include servers and clients similar to server 202 and client 216, respectively, of FIG. 2.

FIG. 6 illustrates an embodiment of a transaction sequence for performing and customizing an allocation statistic trace using a tagging mechanism. In one embodiment, application server 502 includes tagging information module to provide and extract tagging information from the server 502 and expose the tagging information to an object ID service module residing at an on-demand profiling infrastructure 208 at a backend VM 206. The tagging information includes data regarding tags that are associated with threads by users. A tag may include a number that is then associated with a particular thread to obtain customized allocation statistics about the thread having associated the valid tag. First, a thread tag (e.g., a number) is set for a particular thread (or a number of particular threads) 602. This information is then communicated 604 from the application server 502 (via the tagging information module) to the backend VM 206 via the Java server API 204. The information includes associating the particular thread with the thread tag. In one embodiment, as part of the tagging process or mechanism, a user provides certain parameters to request profiles and thus, a request is placed by the user. The request is processed by a request processing module that is part of the tagging information module to process the request and assign a tag to a thread/threads that is/are capable of satisfying the request. A thread may be identified by a numerical value/name (e.g., thread 2000) and may also contain a numerical value.

A request for starting an allocation statistic trace is received 606 at the backend VM 206 via the Java server API 204, and a session of the allocation statistic trace is started 608 via the profiling infrastructure 206 at the backend VM 206 that is the underlying VM. The term “underlying VM” refers to the VM where the profiling is being conducted or where applications, program codes, etc., are being profiled, such as performing an allocation statistic trace for a program code section. Once the allocation statistic trace session is completed 610, the results of these allocations are gathered 612 via the profiling infrastructure 208. In one embodiment, at the profiling infrastructure 208, a determination is made as to whether the thread having performed the allocation has a valid (previously assigned) thread tag associated with it (e.g., whether the thread tag value is greater than 0 (tag value>0)) 614. This technique is to help determine whether the tag (e.g., tag 2000) is really associated with the particular thread. This determination may be performed by the object ID service at the profiling infrastructure 208. If the tag is not valid (e.g., tag value is less than or equal to 0 (tag value≦0)), nothing further is performed 616. If, however, the tag is valid (e.g., tag value is greater than 0), a mapping between the relevant allocation objects (as determined from performing the allocation statistic trace session) and the (previously assigned) thread tag is stored 618, and garbage collection of such objects is performed 620 at the backend VM 620 using a local garbage collector. This information is then used to provide allocation statistics for each thread that is associated with the tag. Any results of the garbage collection (including any garbage collection events) are then reported and gathered 622 at the profiling infrastructure 208. The numerical values provided here are merely used as examples and it is contemplated that any number of values can represent a tag and any number of criteria can be used to evaluate such values to determine the validity of the tag and other relevant items.

In one embodiment, dead objects are removed from the stored mapping 624. These dead objects are detected by determining how an object corresponds to the specified thread tag and by applying the following formula: objects dead=objects generated−objects alive. Furthermore, using the results from garbage collection, the object ID service at the profiling infrastructure 208 detects and determines those objects that are dead and are to be removed. The allocation statistic relating to the dead objects is updated 626, accordingly, at the profiling infrastructure 208. This allocation statistic information is then used by the user to determine system problems (e.g., the source of a memory leak) and eliminate such problems. The stopping of the allocation statistic trace is triggered 628 and communicated to the profiling infrastructure where the allocation statistic traces is stopped 630. Allocation statistics for thread associated with the tag are then reported 632 to the application server 502. For example, for tag 2000, the allocation statistics may include a number of objects allocated (e.g., 20,000 objects allocated), a number of bytes allocated (e.g., 34,000 bytes allocated), a number of objects still alive (e.g., 2,300 objects still alive), and a number of bytes alive (e.g., 23,235 bytes alive), etc.

FIG. 7 illustrates an embodiment of a process for performing and customizing an allocation statistic trace using a tagging mechanism. In one embodiment, at processing block 702, using the tagging mechanism, one or more threads are associated with a thread tag. The tagging mechanism is used to specify the one or more threads, so a customized and specific allocation statistics for these particular threads can be obtained, which not only saves the system its valuable resources, but also provides a user with a more specific set of information to view and evaluate. The tagging information is obtained from an application server via a tagging information module. The tagging process may be initiated by a user via a client by having the user specify a set of parameters to generate a tag and then, associate the tag with a particular thread. The tagging information is then communicated to object ID service at a profiling infrastructure at a backend VM at a server. The server and the client are in communication over a network and associated with an application server environment.

At processing block 704, allocation statistic trace is performed at the backend VM. The request for the allocation statistic trace may be placed by the user via a frontend VM at the client and is communicated to the backend VM via Java APIs, including a Java server API at the server. The allocation report that is obtained as a result of the allocation statistic trace is evaluated at the profiling infrastructure using the object ID server that identifies the a tagged thread, such as determines whether the current thread having performed the allocations has a valid thread tag associated with it at processing block 706. The valid thread tag being the one initially associated with the one or more threads. There may be a number of tags associated with a number of threads and in that case, the determination can be expanded to seek whether the current thread is any of the number of tagged threads, such as whether it has any of the number of tags associated with it. A decision block 708, a determination is made as to the whether the thread has a valid tag associated with it. If not, the process ends at termination block 710. If the thread tag is valid for the thread, any allocated objects (e.g., Java objects) detected as a result of the allocation statistic trace session are mapped to the corresponding tag at processing block 712. The mapping of the objects and the tag is stored, such as in a hash map, at processing block 714.

With the thread tag being valid, garbage collection is performed at the backend VM at processing block 716. A garbage collection report is provided to the profiling infrastructure and the garbage collection is stopped at processing block 718. As a result of the garbage collection report, any dead objects are removed from the stored mapping and the allocation statistic relating to the dead objects is updated, accordingly. The allocation statistic trace stops at processing block 720. A customized allocation statistics for each tagged thread is reported at processing block 722, and the process ends at termination block 710. The customized allocation statistics are customized since they are specifically obtained for those threads that are tagged as per the user's specifications. The allocation statistics may include a number of objects allocated, a number of bytes allocated (e.g., bytes associated with the objects allocated), a number of objects still alive, and a number of bytes alive (e.g., bytes associated with the objects still alive), etc. Also, customized time statistics along with allocation statistics may also be provided. The customized time statistics may provide information about consumed CPU time (e.g., in nanoseconds). These customized statistics are then provided to the user via the client where it can be displayed to the user using a viewer or a display device in communication with the client.

FIG. 8 illustrates an embodiment of a monitoring tool 220. The illustrated monitoring tool 220 includes a JVM monitoring tool (e.g., SAP JVM monitoring tool). Monitoring tool 220 includes a menu line 802 that includes various menu items, such as command, debugging, etc. Monitoring tool 220 further provides a list of VMs 804, and details about any VM 806 which includes a number of items, such as performance, VM info, debugging, trace flags, local memory, etc. Monitoring tool 220 further provides shared memory configuration 808 and shared memory state 810. Monitoring tool 220 is illustrated merely as an example and the embodiment of the present invention are in no way limited to using the illustrated monitoring tool 220.

Processes taught by the discussion above may be performed with program code, such as machine-executable instructions, which can cause a machine (such as a “virtual machine”, a general-purpose processor disposed on a semiconductor chip, a special-purpose processor disposed on a semiconductor chip, etc.) to perform certain functions. Alternatively, these functions may be performed by specific hardware components that contain hardwired logic for performing the functions, or by any combination of programmed computer components and custom hardware components.

One or more modules, components, or elements described throughout this document, such as the ones shown within or associated with the on-demand profiling infrastructure 206 of profiling mechanism 200 of FIG. 2, may include hardware, software, and/or a combination thereof. In a case where a module includes software, the software data, instructions, and/or configuration may be provided via an article of manufacture by a machine/electronic device/hardware. An article of manufacture may include a machine accessible/readable medium having content to provide instructions, data, etc. The content may result in an electronic device, for example, a filer, a disk, or a disk controller as described herein, performing various operations or executions described. A machine accessible medium includes any mechanism that provides (i.e., stores and/or transmits) information/content in a form accessible by a machine (e.g., computing device, electronic device, electronic system/subsystem, etc.). For example, a machine accessible medium includes recordable/non-recordable media (e.g., read only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, etc.), as well as electrical, optical, acoustical or other form of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.), etc. The machine accessible medium may further include an electronic device having code loaded on a storage that may be executed when the electronic device is in operation. Thus, delivering an electronic device with such code may be understood as providing the article of manufacture with such content described above. Furthermore, storing code on a database or other memory location and offering the code for download over a communication medium via a propagated signal may be understood as providing the article of manufacture with such content described above. The code may also be downloaded from a remote computer (e.g., a server) to a requesting computer (e.g., a client) by way of data signals embodied in a propagation medium (e.g., via a communication link (e.g., a network connection)).

FIG. 9 illustrates an embodiment of a computing system 900. Computing system 900 may be used for implementing one or more embodiments of the present invention and for executing program code stored by an article of manufacture. It is important to recognize that the computing system 900 represents merely of various computing system architectures that can be used for the same purposes. The applicable article of manufacture may include one or more fixed components (such as hard disk drive 902 or memory 906) and/or various movable components, such as compact disk (CD) ROM 904, a compact disc, a magnetic tape, and the like. To execute the program code, typically instructions of the program code are loaded into RAM 906. Then, processing core 908 executes the instructions. A processing core may include one or more processors and a memory controller function. A virtual machine or “interpreter” (e.g., JVM) may run on top of the processing core (architecturally speaking) to convert abstract code (e.g., Java bytecode) into instructions that are understandable to the specific processor(s) of processing core 908. Computing system 900 further includes network interface 910 and bus 912 to connect to other systems via a network and to have various components communicate with each other, respectively.

FIG. 10 illustrates an embodiment of a client/server network system 1000 employing an on-demand profiling infrastructure 1018. As illustrated, network 1008 links server 1010 with client systems 1002-1006. Server 1010 includes programming data processing system suitable for implementing apparatus, programs, and/or methods in accordance with one or more embodiments of the present invention. Server 1010 includes processor 1012 and memory 1014. Server 1010 provides a core operating environment for one or more runtime systems (e.g., VM 1016) at memory 1014 to process user requests. Memory 1014 may include a shared memory area that is accessible by multiple operating system processes executing in server 1010. For example, VM 1016 may include an enterprise server (e.g., a J2EE-compatible server or node, Web Application Server developed by SAP AG, WebSphere Application Server developed by IBM Corp. of Armonk, N.Y., and the like). The enterprise server at VM 1016 may host the, on-demand profiling infrastructure 1018. Memory 1014 can be used to store an operating system, a Transmission Control Protocol/Internet Protocol (TCP/IP) stack for communicating over network 1008, and machine executable instructions executed by processor 1012. In some embodiments, server 1010 may include multiple processors, each of which can be used to execute machine executable instructions.

Client systems 1002-1006 may execute multiple application or application interfaces. Each instance or application or application interface may constitute a user session. Each user session may generate one or more requests to be processed by server 1010. The requests may include instructions or code to be executed on a runtime system, such as VM 1016, on server 1010, such as the requests made via the on-demand profiling infrastructure 1018 and its components and modules as described throughout this document.

In addition to what is described herein, various modifications may be made to the disclosed embodiments and implementations of the invention without departing from their scope. Therefore, the illustrations and examples herein should be construed in an illustrative, and not a restrictive sense. The scope of the invention should be measured solely by reference to the claims that follow. 

What is claimed is:
 1. A method comprising: initiating, at a first virtual machine, allocation trace of memory consumption, wherein the first virtual machine resides at an application server of a first computer system; customizing the allocation trace by tagging one or more threads of a plurality of threads associated with the allocation trace, wherein customizing facilitates obtaining customized allocation trace statistic information specific to allocation objects associated with the one or more tagged threads, wherein tagging is performed in runtime, wherein the customized allocation trace statistic information is obtained by performing a memory-based sampling of the allocation objects, wherein the memory-based sampling includes sampling a stack trace to determine an amount of memory allocated to each of the allocation objects in a heap, wherein the amount of memory represents allocated memory consumption of each allocation object; extracting the customized allocation trace statistic information; and providing, from the first virtual machine, the customized allocation trace statistic information to a second virtual machine of the first computer system or a second computer system.
 2. The method of claim 1, further comprising performing an act on the allocation objects based on the customized allocation trace statistic information, wherein performing the act includes initiating garbage collection to remove one or more dead allocation objects or marking the one or more dead allocation objects for a subsequent removal.
 3. The method of claim 1, wherein the customized allocation trace statistic information comprises customized allocation statistics and customized time statistics relating to the allocation objects associated with the one or more tagged threads.
 4. The method of claim 3, wherein the allocation statistics comprise customized data about one or more of the allocation objects allocated to running a user-specified code section, a maximum size of the one or more allocation objects reached during running of the user-specified code section, a number of the allocation objects detected as free objects during the running of the user-specified code section, and a maximum size of the free objects reached during running of the user-specified code section.
 5. The method of claim 3, wherein the time statistics comprise customized data about time consumed by a Central Processing Unit (CPU) during running of the user-specified code section.
 6. The method of claim 1, wherein the first and second virtual machines comprise Java virtual machines, Advanced Business Application Programming (ABAP) virtual machines, or Common Language Runtime (CLR) virtual machines.
 7. An article of manufacture comprising a non-transitory machine-accessible medium having instructions which when executed, cause a machine to: initiate, at a first virtual machine, allocation trace of memory consumption, wherein the first virtual machine resides at an application server of a first computer system; customize the allocation trace by tagging one or more threads of a plurality of threads associated with the allocation trace, wherein customizing facilitates obtaining customized allocation trace statistic information specific to allocation objects associated with the one or more tagged threads, wherein tagging is performed in runtime, wherein the customized allocation trace statistic information is obtained by performing a memory based sampling of the allocation objects, wherein the memory-based sampling includes sampling a stack trace to determine an amount of memory allocated to each of the allocation objects in a heap, wherein the amount of memory represents allocated memory consumption of each allocation object; extract the customized allocation trace statistic information relating to the one or more tagged threads, if the plurality of threads includes the one or more tagged threads; and provide, from the first virtual machine, the customized allocation trace statistic information to a second virtual machine of the first computer system or a second computer system.
 8. The article of manufacture of claim 7, wherein the instructions when executed, further cause the machine to perform an act on the allocation objects based on the customized allocation trace statistic information, wherein performing the act includes initiating garbage collection to remove one or more dead allocation objects or marking the one or more dead allocation objects for a subsequent removal.
 9. The article of manufacture of claim 7, wherein the customized allocation trace statistic information comprises customized allocation statistics and customized time statistics relating to the allocation objects associated with the one or more tagged threads.
 10. The article of manufacture of claim 7, wherein the allocation statistics comprise customized data about one or more of the allocation objects allocated to running a user-specified code section, a maximum size of the one or more allocation objects reached during running of the user-specified code section, a number of the allocation objects detected as free objects during the running of the user-specified code section, and a maximum size of the free objects reached during running of the user-specified code section.
 11. A system comprising: a first computer system having a memory to store instructions for performing customized allocation trace, and a processing device to execute the instructions, wherein the instructions cause the processing device to: initiate, at a first virtual machine, allocation trace of memory consumption, wherein the first virtual machine resides at an application server of the first computer system; customize the allocation trace by tagging one or more threads of a plurality of threads associated with the allocation trace, wherein customizing facilitates obtaining customized allocation trace statistic information specific to allocation objects associated with the one or more tagged threads, wherein tagging is performed in runtime, wherein the customized allocation trace statistic information is obtained by performing a memory-based sampling of the allocation objects, wherein the memory-based sampling includes sampling a stack trace to determine an amount of memory allocated to each of the allocation objects in a heap, wherein the amount of memory represents allocated memory consumption of each allocation object; extract the customized allocation trace statistic information; and provide, from the first virtual machine, the customized allocation trace statistic information to a second virtual machine of the first computer system or a second computer system.
 12. The system of claim 11, wherein the processing device is further to perform an act on the allocation objects based on the customized allocation trace statistic information, wherein performing the act includes initiating garbage collection to remove one or more dead allocation objects or marking the one or more dead allocation objects for a subsequent removal.
 13. The method of claim 11, wherein the customized allocation trace statistic information comprises customized allocation statistics and customized time statistics relating to the allocation objects associated with the one or more tagged threads.
 14. The system of claim 13, wherein the allocation statistics comprise customized data about one or more of the allocation objects allocated to running a user-specified code section, a maximum size of the one or more allocation objects reached during running of the user-specified code section, a number of the allocation objects detected as free objects during the running of the user-specified code section, and a maximum size of the free objects reached during running of the user-specified code section.
 15. The system of claim 13, wherein the time statistics comprise customized data about time consumed by a Central Processing Unit (CPU) during running of the user-specified code section.
 16. The system of claim 13, wherein the first and second virtual machines comprise Java virtual machines, Advanced Business Application Programming (ABAP) virtual machines, or Common Language Runtime (CLR) virtual machines.
 17. The system of claim 11, wherein the application server comprises a Java application server having a Java 2 Enterprise Edition (J2EE) engine. 